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masif

MaSIF- Molecular surface interaction fingerprints. Geometric deep learning to decipher patterns in molecular surfaces.

mask-yolo

Inspired from Mask R-CNN to build a multi-task learning, two-branch architecture: one branch based on YOLOv2 for object detection, the other branch for instance segmentation. Simply tested on Rice and Shapes. MobileNet supported.

maskrcnn-benchmark

Fast, modular reference implementation of Instance Segmentation and Object Detection algorithms in PyTorch.

mass-ts

MASS (Mueen's Algorithm for Similarity Search) - a python 2 and 3 compatible library used for searching time series sub-sequences under z-normalized Euclidean distance for similarity.

massformer

Tandem Mass Spectrum Prediction with Graph Transformers

mat2spec

mat2vec

Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

matador

⚗️ matador is an aggregator, manipulator and runner of first-principles calculations, written with a bent towards battery 🔋 electrode materials.

matbench

Matbench: Benchmarks for materials science property prediction

matdeeplearn

MatDeepLearn, package for graph neural networks in materials chemistry

matdesinne

Inverse materials design via invertible neural networks

material-design-icons

Material Design icons by Google

material_informatics

Basic components to perform material informatics: modeling (GPR, KRR, XGB, NN, RF, linear, and ensemble learning of them), backward prediction, multi target screening, etc.

material_simulation_scripts

Useful scripts for material simulation software & pkgs

materialdesigninxamltoolkit

Google's Material Design in XAML & WPF, for C# & VB.Net.

materials-modeling

Programming tutorials in Python3 for the Materials Modeling course at IISc-2018

materialsdiscoveryml

matgen

The inverse materials design is a key topic of materials science nowadays. The proposed software solutions are useful tools for decision support at a pre-synthetic stage. Though, the existing methods are restricted by predefined elemental composition and can search for new materials only in a small part of an entire chemical space. Here we would like to present the machine-learning approach i.e. free from the mentioned restriction and able to propose novel materials with different elemental compositions and crystal structures. The method was tested on generating super-hard materials and proved and ability to generate well-known oxides or carbides, as well as novel compounds with three or four elements inside.

matgenb

Jupyter notebooks demonstrating the utilization of open-source codes for the study of materials science.

matgnn

MatGNN is a GNN pipeline model for materials science discovery

math-comp

Mathematical Components

mathematical-python

Introduction to Mathematical Computing with Python and Jupyter

mathematics-for-ml

🧮 A collection of resources to learn mathematics for machine learning

mathfordeeplearning

Source code for the book "Math for Deep Learning"

mathy

A platform for using computer algebra systems to solve math problems step-by-step with reinforcement learning

matminer

Data mining for materials science

matnet

Motion-Attentive Transition for Zero-Shot Video Object Segmentation (AAAI2020&TIP2021)

matplotx

:bar_chart: More styles and useful extensions for Matplotlib

matscipy

Materials science with Python at the atomic-scale

matsynthesis

Materials Synthesizability Prediction